Prediction-based Physical Layer Base Station Switching using Imaging Data

Abstract

Deep learning is applied to implement base station switching in physical layer using imaging data for 60 GHz millimeter-wave communications where the received signal is susceptible to blockage. In particular, a predictive model is trained from video frames and received signal data. Accordingly, the video frames are used to predict received power two seconds ahead using three-dimensional convolutional neural networks and long short-term memories, followed by proactive switching decisions. The model can predict the future received power with root-mean-square errors under 2 dB. The proposed prediction-based proactive switching method surpasses the reactive approach in terms of connected duration, maintaining a stable connection in various blockage moving trajectories.